This article explored the relevant standard requirements that need to be referenced when conducting hand-transmitted vibration tests on medical electrical (ME) equipment. It pointed out that the difficulties in conducting these tests arised from issues such as low integration, unclear applicability, and excessive lag in domestic method standards. The article also provided a detailed analysis of the testing equipment required for hand-transmitted vibration tests, sensor installation, and sample arrangement. It highlighted that when conducting hand-transmitted vibration tests on ME equipment according to then current relevant national standards, the complexity and difficulty of achieving a suitable testing environment were due to the lack of compatibility among multiple method standards with significant time spans. Finally, the article proposed reasonable suggestions aimed at improving the inspection efficiency of medical device manufacturers and relevant inspection and testing institutions, and ensuring the accuracy of inspection and testing.

Objective To design a respiratory monitoring device based on thoracic impedance measurement method to meet the demand for accurate monitoring of respiratory function. Methods The respiratory measurement equipment was designed by using the thoracic impedance measurement method, and its host computer program was designed. The digital and analog resources within the CY8C5888 on-chip system were utilized by the hardware part fully to achieve chest impedance measurement on a single chip, and the external Bluetooth module were combined to realize wireless data transmission. The host computer software was developed by LabVIEW and could accept impedance data, filter the data, analyze the respiratory rate and monitor abnormal respiratory rates. Among them, the respiratory rate of the respiratory signal was measured by the dynamic differential threshold method, which could effectively adapt to signal changes and improve the accuracy and real-time performance of detection. Results The respiratory monitoring system was small in size and suitable for wearable measurement. The host computer interface could display the respiratory rate waveform. After actual verification by the human body, the average error between the measured respiratory rate and the actual respiratory rate was 3.54%. The error was generally maintained within an acceptable range and met the technical index requirements for respiratory rate in JJG 1163-2019 Verification Regulation for Multi-Parameter Monitor. It could measure and record breathing signals in real time and issue alarms for abnormal signals. Conclusion This device can effectively adapt to signal changes and improve the accuracy and real-time performance of detection.

Objective To solve the problems of low efficiency and high error rate of manual detection of the third phase information (batch number, production date and expiration date) of drug packaging in the traditional drug production process, this study designs an optical character recognition system by combining optical image optimization and deep learning algorithm improvement. Methods The hardware detection platform of multi-angle LED light source and industrial camera was built to enhance the image quality of imprinted characters by combining with photometric stereoscopic technology. The software algorithm adopted the improved DBNet detection model (integrated deformable convolution module and feature pyramid enhancement module-feature fusion module architecture) and ABINet recognition model with integrated deformable attention mechanism to improve the text detection and low-quality character recognition capability in complex scenes. Results The recognition accuracy of the system was 89.0% in the test of 1200 low-quality drug images, which was 3.7% higher than the traditional ABINet model. At the same time, in the production line measurement, the detection accuracy of 5 types of drug packaging reached more than 98.5%, the average system detection time was about 1/4 of the average manual detection time. Conclusion Through hardware imaging optimization and algorithm architecture innovation, this study effectively solves the problem of drug information recognition in pharmaceutical companies that is easy to make mistakes and difficult to control.

Objective To develop and design a device for testing whether the assembly joint of the anesthesia breathing tube can be connected to the standard joint (YY/T 1040.1-2015) for connection test and whether separation occurs under a force value of 40 N after connection. Methods Based on the requirements in the standard, the standard joint was connected with the assembly end of the pipeline according to the requirements. At least 150 mm away from the end of the pipeline, a tensile force was applied along the direction of the pipeline axis at a rate of (50±5) mm/min, and the separation of the joint was recorded. The test was carried out in an environment of (42±3) ℃. The S7-200 SmartPLC was used as the main controller, including the rotary force measuring module, human machine interface touch screen, execution module, box heating module, hardware system and other control modules to control the force value in the docking test, the rotation Angle, and the rate and temperature in the tensile test. The inspectors conducted connection and tensile tests on the breathing tubes in the constant-temperature insulated box through the human-machine interaction interface. Results According to the requirements, the equipment structure was designed, and the finite element analysis of important components was conducted using the Simulation plugin in SolidWorks. The maximum displacements of the rotating force measurement bracket and the pipeline clamping device were (0.0366±0.0008) and (0.0205±0.0005) mm respectively. Compared with the structure without ribbed plates, the differences were all statistically significant (P<0.001), verifying the stability of the entire institution. Conclusion This equipment can be used in actual production and quality inspection, and can ensure the connection safety of anesthetics and ventilator breathing tubes.

Objective To avoid the injury of patients caused by intubation errors, to study the electromagnetic positioning detection method of tracheal intubation trajectory, so as to improve the success rate and efficiency of intubation operation and provide key technical support for the development of automated tracheal intubation robot. Methods The correct and incorrect tracheal intubation curves were collected based on the electromagnetic position tracker and the human head and neck model. The curve data were processed and screened using MATLAB. The optimal paths of the correct and incorrect tracheal intubation curves were calculated based on the dynamic time warping (DTW) algorithm. The DTW algorithm was then used to calculate the similarity between the correct and incorrect optimal paths and the curve to be detected. The correctness of this intubation operation was determined by comparing the similarity values. Results The similarity values between 120 groups of intubation operations and the correct and incorrect optimal paths differed significantly, which could be used to determine whether the intubation operation was correct or not. The system was tested using 50 sets of curves, and the success rate was 100%. Conclusion The tracheal intubation trajectory detection system can detect whether the intubation process is correct, assist doctors in operation, improve intubation efficiency, and avoid accidental injury to patients during intubation.

Objective By analyzing the working principle of the intelligent infrared thermometer and conducting temperature measurement experiments on the human body for verification, to study the influence of various factors such as the distance and angle between the measurement target and the intelligent infrared thermometer, as well as environmental temperature and humidity, on the results of the intelligent infrared thermometer. Methods Based on the principle of infrared temperature measurement, a walk-in environmental test chamber was used to simulate the temperature measurement conditions of the intelligent infrared thermometer under different temperatures and humidity. The data were analyzed using SPSS 22.0 statistical software, fully considering the influence of multiple factors such as distance, angle, temperature and humidity on the intelligent infrared thermometer. Results When only height was considered, there was a statistically significant difference in the measurement results of volunteers with a height of 190 cm using a glass thermometer and an intelligent infrared thermometer (P<0.05). There was no statistically significant difference in the measurement results between the glass thermometer and the intelligent infrared thermometer of the volunteers with a height of 160 cm (P>0.05). The measurement results were less affected by angle changes. However, angle alterations could have an impact on the measurement data (resulting in lower measurement values), especially when the measurement distance increased. When only the environmental temperature and humidity were considered as single factors, there were statistically significant differences in the measurement data when comparing the ambient temperature between 25℃ and 35℃, as well as between 25℃ and 10℃, and between 35℃, 30%RH and 35℃, 80%RH (P<0.05). With the interference of multiple factors, the accuracy of the measurement data would decline (the measured value will be lower). When considering the simultaneous interference of multiple factors such as distance, height and humidity, the maximum relative error was 2.7% and the maximum error was 1℃. Conclusion In practical application, the effective measurement distance of the intelligent infrared thermometer and the influence of environmental temperature and humidity on the temperature measurement results should be fully considered. When measuring temperature, the person being measured should be kept within the effective measurement distance. When the environmental temperature and humidity are too low or too high, measures should be taken to maintain the stability of temperature and humidity in the usage site and reduce the impact caused by adverse environmental factors.

Objective To address the problems of low triage efficiency, inaccurate triage and privacy leakage in traditional triage methods, to establish an intelligent triage model, so as to enhance the efficiency, accuracy and privacy protection ability of triage. Methods The triage algorithm was designed by integrating principal component analysis and random forest algorithm, and differential privacy and homomorphic encryption technology were introduced to construct a dual-encrypted hospital emergency triage model. Results The triage algorithm designed in the study achieved convergence after the 4th and 17th iterations respectively in the tests on two datasets. The fitting degrees were 96.8% and 98.4% respectively. It outperformed other algorithms in terms of computational efficiency and triage accuracy. The average calculation time of the research model was 0.43 s. The triage accuracy was higher than that of other triage models within the range of the total budget value for privacy protection ranging from 0.01 to 5.00. There were statistically significant differences in the waiting time for consultation, triage accuracy and readmission rate before and after the application of the emergency triage model (P<0.05), indicating that the model in this study significantly improved the triage capacity of the hospital. Conclusion Under the condition of safeguarding patients’ privacy, the model in this study can efficiently and accurately complete triage and improve the utilization rate of medical resources.

Objective To study the variation pattern of the functional connectivity characteristics of the cerebral cortex with mental load under different types of visual information mental load tasks. Methods Based on the N-Back paradigm, brain-load tasks of different information types and difficulties were designed. During the task execution, 60-channel electroencephalogram (EEG) signals of 18 subjects were synchronously collected. The source location of scalp electroencephalogram signals was carried out based on the sLORETA method, and the Theta and Alpha band cortical EEG functional networks were constructed based on the Pearson correlation method. Statistical analysis methods were used to screen the network nodes and global indicators that showed significant changes with the mental load under each task. Results Among the 4 tasks, the degree values of 6 nodes in 2 frequency bands and the connection strengths of 4 groups of functional connections changed with the variation of mental load, and the differences were statistically significant (P<0.05). With the increase of mental load, the average characteristic path length of the Theta frequency band network decreased from (2.8427±0.0083) to (2.7751±0.0051). The average clustering coefficient and global efficiency increased from (0.3384±0.0018) and (0.4232±0.0015) to (0.3620±0.0020) and (0.4432±0.0017), respectively. Conclusion Functional connectivity indicators of the cerebral cortex can effectively represent the changes in mental load under different visual information memory tasks.

Objective By analyzing the protective effects of lead screens with different lead equivalents on different types of medical diagnostic X-ray machines, to provide guidance for medical staff and accompanying personnel in choosing appropriate lead screens. Methods The X-ray energy spectrum was simulated by the Monte Carlo method when the tube voltage was 50-140 kV, the total filtration thickness was 0.5-5.0 mm (Al), and the anode angle was 12°. Taking this as the source term, the normalized dose rate and attenuation factor of lead screens after X-ray shielding were simulated, and the actual protective effects of lead screens with different lead equivalents on different X-ray machines were evaluated in combination with experimental measurements. Results The attenuation factor of lead screens increased with the increase of lead equivalent. When it exceeded 1.5 mmPb, the attenuation factor increased significantly. A 1.0 mmPb lead screen could effectively shield the X-rays generated by a 60 kV X-ray machine with a tube voltage. If the distance between the machine head and the lead screen was more than 3 m, a 1.0 mmPb lead screen could reduce the dose rate of the machine head to below 1 µSv/h at a distance of 1 m in any direction. Conclusion The tube voltage of the X-ray machine, the filtration thickness, the lead equivalent of the lead screen and the protective distance are important factors affecting the protective effect of the lead screen. 1.0 mmPb lead screen can effectively shield X-ray machines such as dental X-ray machines with tube voltages set below 60 kV. For X-ray machines with tube voltages set below 100 kV, such as digital subtraction angiography (DSA), oral CT, and mobile C-arm, a 2.2 mmPb lead screen can be selected at a distance of 0.5 meters from the machine head and a 2.0 mmPb lead screen at a distance of 1 meter from the machine head, while avoiding the primary radiation. For X-ray machines with tube voltages such as digital radiography (DR) and CT set above 140 kV, distance protection should be given priority consideration.

Objective To design a health management platform based on mobile Internet of Things (MIoT) for realize the intelligent management of medical equipment. Methods The health management platform based on MIoT was constructed with a system architecture composed of perception layer, network layer, platform layer, and application layer. The perception layer realized data acquisition via three-dimensional acceleration sensor and radio frequency identification tag. The network layer used 5G slicing technology combined with wireless intrusion detection system and wireless network controller to transmit data. The cloud platform integrated real-time stream processing and batch analysis engine. The application layer realized intelligent management of medical equipment through intelligent algorithm. The number of medical equipment deployments, response time of equipment deployment, number of deployment errors, average maintenance cycle of equipment, qualified rate of final maintenance of equipment and maintenance and repair costs before and after the application of MIoT-based health management platform were compared. Results After the application of health management platform based on MIoT, the utilization rate of medical equipment, the number of medical equipment deployment and qualified rate of final maintenance of equipment were significantly improved, and the differences were statistically significant (P<0.05). Meanwhile, the response time of equipment deployment, number of deployment errors, average maintenance cycle of equipment, operation and maintenance expenditure and maintenance and repair costs were significantly reduced, with statistically significant differences (P<0.05). Conclusion The health management platform based on MIoT can significantly improve the efficiency of medical equipment in the intelligent management of medical equipment, reduce the maintenance cost of medical equipment, and provide a reference for the intelligent management of hospital medical equipment.

Objective To design a monitoring and control system for the medical equipment deployment center based on electrical energy management to ensure the stable battery status of medical equipment in the deployment center, reduce safety risks, and alleviate the burden on management personnel. Methods The system used an electrical energy data acquisition and control module to monitor the charging status of the equipment, and achieved data transmission via Wi-Fi. A TCP server, MySQL database, and Django web platform were built on the PC end to provide real-time monitoring and remote control for management personnel. Results After verification, the system could retrieve the charging status of 8 connected devices after logging in to the web page, and the power on/off operation for the specified device could be completed within 3 s after issuing the control command. Compared with the control group, the experimental group showed significant improvements in the frequency of management personnel visiting the deployment center, the time for charging and power-off, and the number of devices with battery level ≥50% during inspection, and the differences were statistically significant (all P<0.05). Conclusion This system effectively ensures the stable and safe battery status of the equipment, significantly reduces the workload of managers, and enhances the emergency response capability and intelligent management level of equipment of the deployment center, it provides strong support for the stable operation of hospital departments.

Objective To address the challenges posed by traditional medical equipment management models in meeting the needs of smart hospitals, especially in terms of equipment usage management, aiming to construct a medical equipment usage assessment and management system based Internet of Things (IoT). Methods Based on IoT technology, combined with digital imaging and communications in medicine, optical character recognition + artificial intelligence recognition, current bypass detection and other methods, the IoT access of various medical devices of the Affiliated Hospital of Jiangnan University was realized, and the device performance before and after the system was launched was compared. Results After the system was launched, a longitudinal analysis was conducted on the performance of 28 large imaging devices. Compared with the same period in 2023, the equipment utilization rate increased by 4.55% (P=0.030), the cost yield rate improved by 15.89% (P=0.002), the fault response time was shortened and service satisfaction increased (P<0.001) in 2024. Conclusion The IoT based medical equipment usage assessment and management system effectively solved issues such as data silos, uneven management between two campuses, and data security, significantly enhancing the management level and usage efficiency of medical equipment.

Objective To study the consistency and standardization issues of technical requirements for medical device products after registration and listing. Methods From the perspective of medical device product registration and evaluation, the technical requirements of medical device products and the preparation requirements of change comparison table were analyzed through questionnaire survey. Based on the WPS Office platform, a management system for the technical requirements and basic information of medical device products was developed by using VBA. Results A product technical requirement content data framework with a single clause as the minimum unit was constructed, and this data framework was applied to its information management system. This research had achieved automatic standardization of the technical requirements format for medical device products, assisting in the preparation of product technical requirements and product registration information change comparison tables. Compared with conventional operating methods, the average preparation cycle of product technical requirements was shortened by 30.5%, the average preparation cycle of product technical requirement change comparison tables was shortened by 70.2%, and the number of compilation errors was reduced by 17.6%. Conclusion The management system and data framework discussed in this article offers an innovative solution for the internal management of medical device product technology. Furthermore, it facilitates the seamless integration of various stages in the medical device lifecycle, including inspection and evaluation.

Objective To analyze the current situation and problems of medical device adverse event monitoring and post-market safety evaluation conducted by the first batch of national medical device adverse event monitoring sentinel hospitals (referred as national sentinel hospital) in China, as to provide reference and suggestion for the construction of management standards for national monitoring sentinel in China. Methods In this study, the expert discussion method was adopted to design a questionnaire on the current situation of adverse event monitoring of medical devices in sentinel hospitals in China from five dimensions: institutional construction, personnel and facility allocation, construction of adverse event monitoring system, adverse event monitoring, and post-marketing safety evaluation research. Questionnaire surveys and field investigations were conducted at the first batch of 105 national monitoring sentinel sites in China. Statistical and descriptive analyses of the investigation results were carried out and then targeted improvement suggestions were put forward. Results A total of 92 national sentinel hospitals provided valid responses, with an effective recovery rate of 87.6%. At present, 92 national sentinel hospitals have established a basic system for monitoring adverse events of medical devices. Among them, 68.5% have set up specialized monitoring institutions, 90.2% have equipped full-time or part-time monitoring personnel, 95.7% have carried out business training, and 98.9% have formulated and implemented relevant adverse event monitoring systems. However, further strengthening and improvement were needed in risk management and post-market safety evaluation of medical devices. Conclusion Sentinel hospitals should enhance risk management and post-market safety evaluation efforts for medical devices. It is suggested that regulatory authorities should promptly issue relevant guidelines and provide technical training.

Objective To explore the key points of biological evaluation for medical aesthetic devices, ensuring their safety and efficacy in clinical applications, as to provide scientific evidence for industry regulation and technical review, and promoting standardized industry development. Methods The medical aesthetic devices that have been marketed domestically and internationally were collected and analyzed. Combined with relevant domestic and international regulations and technical standards, and based on the physicochemical properties and clinical application characteristics of medical aesthetic devices, common issues in their biological evaluation were analyzed, such as the selection of evaluation pathways, preparation of test solutions, and interfering factors in biocompatibility tests, and proposed potential solutions. Results At present, most medical aesthetic devices still adopt the endpoint evaluation pathway for biological evaluation. Liquid or gel-like medical aesthetic devices had a relatively higher probability of interference during biocompatibility tests. Therefore, in specific biocompatibility tests, it was necessary to fully consider the physicochemical properties of the products and their clinical application scenarios. Conclusion Biological evaluation is a critical safeguard for the safe clinical application of medical aesthetic devices. It must be conducted scientifically and rationally based on device characteristics to ensure compliance with safety requirements.

Objective To effectively regulate the management of medical measurement devices in hospitals, ensure the accuracy of their measurements, reduce the clinical use risks of medical devices, and improve medical quality. Methods This study innovatively established a model that deeply integrates the Plan-Do-Check-Act (PDCA) cycle with digital management, breaking through the limitations of traditional, singular management approaches. By utilizing the Man, Machine, Material, Method, and Environment (5M) analysis and Pareto analysis, it summarizes the primary causes of low detection coverage. Based on these primary causes, corresponding countermeasures are proposed using the "5W1H" method. Statistical analysis was conducted on the indicators of the missed inspection rate of measuring instruments before and after implementation through systematic comparison. Results After implementation, the inspection coverage rate increased from 88.68% to 100.00%, and the missed inspection rate of measuring instruments in use at the hospital was successfully reduced from 11.32% to 0%. Additionally, the number of manual verification errors and omissions decreased from 115 sets of equipment to 0. Conclusion It has been verified that the implementation of the PDCA cycle can significantly control the missed inspection rate of measuring instruments, while also contributing to strengthening the risk management of medical devices and ensuring their safety in clinical use.

Objective To explore the development path of the medical device industry in Henan Province, and provide reference and guidance for its better development. Methods By using the SWOT analysis method, which was based on the situation analysis of the internal and external competitive environment and competitive conditions, the factors influencing the development of the medical device industry in Henan Province, namely strengths (S), weaknesses (W), opportunities (O) and threats (T), were identified. The 37 influencing factors were scored using a survey questionnaire scoring method, and statistical analysis and cross research were conducted on the survey data. Results The effective response rate of this questionnaire was 93.3%, with high credibility (Cronbach α coefficient value of 0.966, CITC value > 0.4) and good validity (KMO test value of 0.933, factor loading coefficient > 0.4). Among them, there was no significant difference in the scores of 32 factors (P>0.05), and 5 factors showed significant differences in scores due to the different cognition of the surveyors (P<0.05). Based on the analysis and the average score, 20 key influencing factors were identified and a SWOT strategic matrix for industrial development was constructed. Conclusion In response to the weak links in the development of the medical device industry in Henan Province, it is proposed to seize future market demand, vigorously develop advantageous devices such as medical consumables and rehabilitation equipment, and compete with industry advantage regions to form a "honeycomb effect". It is also suggested to build a highland of medical resources and stimulate innovation vitality through multi-party collaboration between medical and engineering fields.

Objective To establish a scientific, subjective and objective evaluation index system for medical magnetic resonance equipment clinical application, which provide theoretical support for the systematic clinical evaluation of medical magnetic resonance equipment. Methods Through literature research, enterprise communications and other methods, the evaluation indexes of medical magnetic resonance equipment were preliminarily formulated. Relevant experts from clinical and medical engineering were organized to carry out expert consultation, and the final evaluation indexes were determined through the Delphi method, the indexes’ weights were determined through the analytic hierarchy process, so as to form the evaluation index system of medical magnetic resonance equipment. Results A evaluation index system for medical magnetic resonance equipment was constructed, covering three dimensions of technical performance, imaging effect, and operational performance, and containing three first-level indexes as well as 15 second-level indexes, all demonstrating high consistency. Among these, indicators such as "signal-to-noise ratio", "contrast", "spatial resolution" and "slice thickness in 2D scanning" were assigned relatively higher weights. Conclusion The evaluation index system of medical magnetic resonance equipment clinical application established in this study can systematically evaluate the clinical application of medical magnetic resonance equipment from multiple aspects. It can provide directions for the improvement and optimization of domestic medical magnetic resonance equipment, and help the promotion and application of domestic medical magnetic resonance equipment.

Objective To analyze the current situation of class B large medical equipment allocation in China and the level of coupling coordination with high-quality economic development, identify obstacles factors, and provides a reference for the rational optimization of class B large equipment allocation. Methods The entropy weight method and comprehensive evaluation method were used to comprehensively evaluate the configuration of class B large medical equipment and high-quality economic development, and the coupling coordination degree model was used to measure the level of coupling coordination between them. The main obstacle factors affecting the coupling coordination were calculated using the obstacle function. Results At the end of the "Thirteenth Five-Year Plan" period, the stock of class B medical equipment in China was 4501 units, and the "Fourteenth Five-Year Plan" targets 3528 units. The total amount of class B medical equipment is insufficient and shows a pattern of East > Central > West. Among the 31 provinces, 8 were in a state of high-quality coordinated development, 19 were in a state of good coordinated development, and 4 were in a state of moderate coordinated development. The average coupling coordination degrees for the eastern, central, and western regions are 0.8291, 0.7861 and 0.7261, respectively. The number of class B large medical equipment, the ownership of class B large medical devices per million population, the level of economic green development, and the level of economic coordinated development are the main obstacle factors that constrain the coupling coordination level of this binary composite system. Conclusion Class B large medical equipment still requires government planning improvement; administrative departments at all levels should play a leading role in science, rationally allocate class B large medical equipment; adhering to the concept of green, shared, and coordinated development, according to regional resource endowments, promote the coordinated development of the two systems in a manner adapted to local conditions.

Objective By analyzing the previous literature data in the field of medical engineering, to reveal the research trends and hot topics in this field. MethodsThe PubMed database and the Medical Pulse bibliometric analysis platform were used to systematically review the medical engineering research literature published from 2014 to 2024. The R package “bibliometrix” (version 4.2.3) was adopted to conduct data mining on the retrieved literature. Results A total of 3279 articles related to medical engineering were retrieved through result search, with a total of 2589 citations. The peak number of published articles in 2023 was 442. The journal “Heliyon” had published the largest number of related documents, reaching 138. The top two authors in terms of the number of published articles were from the United States and China respectively. The top three research institutions in terms of the number of published papers were all from the United States. Both the United States and China had actively participated in international cooperation in the field of medical engineering. The key words such as “biomedical engineering” “tissue engineering” and “drug delivery” had been the hot research topics in the field of medical engineering in the past decade. High-impact factor articles had relatively concentrated themes, while highly cited articles had scattered themes. Conclusion By analyzing the hot topics and development trends in the field of medical engineering in the past 10 years, this study provides a strong basis for practitioners to understand their own positioning and choose research topics.

Objective To explore and analyze the individual influencing factors of the size-specific dose estimate (SSDE) based on water equivalent diameter (WED) (that is SSDE WED) in adults undergoing non-contrast chest computed tomography (CT) examinations, and to construct a regression prediction model for SSDE WED. Methods A total of 205 adult subjects who underwent chest CT were included. Cross-sectional images at the central level of chest CT were selected, and the region of interest (ROI) was delineated. The cross-sectional area of ROI (A_ROI) and mean CT value of ROI (CT_ROI) were recorded to calculate SSDE WED. Correlation analysis and multiple linear stepwise regression analysis were used to explore the relationship between SSDE WED and individual influencing factors, and a prediction model was established. Results Correlation analysis showed that SSDE WED was significantly positively correlated with A_ROI, anterior-posterior diameter, body weight, fat volume, lateral diameter (D_LAT), muscle volume, mean CT value of fat, gender, height, body mass index, fat content, and CT_ROI (all P<0.05). Multiple linear stepwise regression analysis identified independent influencing factors for predicting SSDE WED, including gender, body weight, D_LAT, A_ROI and CT_ROI (all P<0.05). A prediction model was further established, which showed good fitting (F=116.817, P<0.05, R=0.864, R²=0.746, adjusted R²=0.739) and could be used to predict SSDE WED. Conclusion The SSDE WED prediction model based on five independent influencing factors (gender, body weight, D_LAT, A_ROI, and CT_ROI) is helpful for personalized assessment of the actual radiation dose received by adult subjects undergoing chest CT examinations.

Objective To investigate the impact of different scout scan parameters on image quality and radiation dose in both axial and helical chest CT scans of a phantom using automatic tube current modulation (ATCM) technology. Methods Scout scans of the chest phantom were performed with a fixed tube current of 10 mA, and different combinations of tube voltage, X-ray tube projection positions, and scan speeds were used. Diagnostic CT (DCT) scans were conducted at 80 kV with ATCM, involving axial and helical scans of the phantom chest, and 144 sets of data were acquired. Regions of interest were delineated on the same slices of the acquired images to measure CT values and standard deviation. The contrast-to-noise ratio (CNR) and figure of merit (FOM) were calculated, and the volume CT dose index (CTDI_vol) and tube current were recorded. CNR, FOM, and CTDI_vol were compared under different parameter combinations. Results The overall CNR of helical DCT scans was significantly superior to that of axial scans (P<0.05). For helical scans with the same speed, significant differences in CNR were observed among different scout tube voltages (P<0.05). At a scout tube voltage of 80 kV, CNR differed significantly between scan speeds of 100 and 200 mm/s (P<0.05). The overall FOM of axial DCT scans was superior to that of helical scans (P<0.05). For helical scans at 200 mm/s, significant differences in FOM were observed among different tube voltages (P<0.05). At a scout tube voltage of 80 kV, FOM differed significantly between 100 and 200 mm/s (P<0.05). The overall CTDI_vol of helical DCT scans was significantly higher than that of axial scans (P<0.05). For helical scans with the same speed, significant differences in CTDI_vol were observed among different tube voltages (P<0.05). At a scout tube voltage of 80 kV, the CTDI_vol of axial scans at 100 mm/s was significantly higher than that at 200 mm/s (P<0.05). The CTDI_vol of single anteroposterior scout was lower than that of lateral scout. The CTDI_vol of dual scouts in the sequence of "lateral + anteroposterior" was lower than that in "anteroposterior + lateral" (except for partial equality between 90°+180° and 180°+90°). For helical scans, the CTDI_vol was higher when the last X-ray tube angle was 180° than when it was 0°. Conclusion Scout scan parameters (tube voltage, scan speed, X-ray tube projection angle) have significant impacts on image quality and radiation dose in 80 kV ATCM-based DCT scans of the phantom chest. Moreover, radiation dose is associated with the projection position and scanning sequence of the scout X-ray tube.

Objective To explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating benign from malignant hyperintense liver masses in the hepatobiliary phase (HBP). Methods A retrospective analysis was performed on the clinical data of 81 patients with HBP hyperintense liver masses admitted to the hospital from January 2022 to February 2024. The nature of the masses was confirmed by biopsy or surgery. All patients underwent DCE-MRI before operation or biopsy. Parameters including maximum slope of decrease (MSD), positive enhancement integral (PEI), mean enhancement time (MET), and maximum slope of increase (MSI) were measured. These parameters were compared between benign and malignant HBP hyperintense liver masses. Receiver operating characteristic (ROC) curves were plotted to analyze the value of DCE-MRI in differentiating the nature of these masses. Results A total of 98 lesions were identified in 81 patients, including 46 malignant and 52 benign lesions. PEI, MET, and MSI in patients with benign HBP hyperintense lesions were significantly higher than those in patients with malignant ones (P<0.05). ROC curve analysis for diagnosing the nature of HBP hyperintense liver masses showed that PEI had the highest area under the curve (AUC) of 0.985, while the combined diagnosis of these parameters had an AUC of 0.719. Specifically, for differentiating subtypes of malignant HBP hyperintense liver masses: MSI showed the highest AUC (0.821) for hepatocellular carcinoma; MET had the highest AUC (0.831) for intrahepatic cholangiocarcinoma; MET exhibited the highest AUC (0.964) for hepatic vascular tumor; and MSI had the highest AUC (1.000) for liver metastases. Conclusion DCE-MRI parameters are of high value in identifying the nature of HBP hyperintense liver masses, with PEI being the most effective. Moreover, these parameters demonstrate high diagnostic efficacy for different types of malignant HBP hyperintense liver masses. Clinically, DCE-MRI parameters can be combined to differentiate benign from malignant HBP hyperintense liver masses.

Medical auxiliary diagnosis has become a critical research direction in deep learning. By leveraging multi-layer neural networks, deep learning enables the realization of non-linear mapping between inputs and specific outputs through extracting deep features from image data, particularly in capturing lesion features and revealing details that are inaccessible to the naked eye. The applications of deep learning in the field of gastric cancer have covered multiple key aspects, providing multi-dimensional technical support for the precise diagnosis and treatment of gastric cancer. This review mainly focused on the applications of deep learning networks in the precise automatic classification of gastric cancer, the relationship between deep features and genes, and prognostic evaluation, with an emphasis on summarizing the basic ideas and improvements of the models. It also summarized the existing problems and challenges in this field and prospects future research directions, aiming to provide new perspectives and methods for computer-aided diagnosis and offer diverse entry points for the accurate diagnosis and personalized treatment of patients.

Telemedicine, leveraging its advantages of breaking time and space barriers and enabling bidirectional data feedback and sharing, has emerged as a novel medical model for medical communication between patients with diabetic foot ulcer (DFU) and healthcare providers. With the rapid advancement of wearable devices, big data, artificial intelligence, and other technologies, a new wave of telemedicine development has been triggered, which has demonstrated unique strengths in the monitoring and management of DFU. However, due to technical limitations, risks of information and data leakage, and inadequate supporting policies, there remains a gap between practical application and clinical promotion of telemedicine. This paper elaborated on the concept of telemedicine, summarized its application forms in the monitoring and management of patients with DFU, and explored the acceptance of telemedicine among these patients and their relevant stakeholders. It also analyzed the shortcomings and challenges in its promotion, and put forward prospects and considerations, aiming to provide references for clinical research on telemedicine in patients with DFU, thereby alleviating the enormous pressure on resource allocation and operation management of the healthcare system.

The applications of new-generation information technologies such as big data, artificial intelligence, the Internet of Things, block chain, and virtual reality in medical equipment management are continuously innovating, which is expected to promote the transformation of medical equipment quality control models and improve the quality management level of medical institutions. Based on the current status and challenges of medical equipment quality control, this paper summarized the applications of new-generation information technologies in key links of medical equipment quality control, including data collection, intelligent analysis, information traceability, and simulation optimization. It also analyzed relevant cases and prospects for future trends, aiming to facilitate the reengineering and reform of quality management processes in medical institutions and drive medical equipment quality management towards intellectualization, networking, and refinement. Medical institutions should strengthen collaboration, transform innovative achievements, establish a full-process quality and safety traceability mechanism and a co-governance system, and enhance their safety assurance capabilities.

As a key energy device in modern minimally invasive surgery, the radiofrequency-ultrasonic scalpel, featuring the simultaneous delivery of both radiofrequency and ultrasonic energies, boasts significant advantages such as minimal trauma and rapid recovery, thus being widely used in clinical practice. Compared with traditional instruments, it performs better in reducing operation time, intraoperative bleeding, and postoperative complications. Currently, this device has played an important role in multiple fields including general surgery, urology, gynecology, and neurosurgery. Although domestic research and development have achieved remarkable outcomes, with domestic substitution realized in some fields, issues such as reliance on imported core materials and limited expansion in the international market remain. This review mainly summarized the structural principles, application scenarios, and innovation directions of the radiofrequency-ultrasonic scalpel, analyzed its prospects, and put forward improvement suggestions, aiming to provide references for its subsequent development.

Currently, the incidence of breast cancer remains high. As a crucial procedure following mastectomy, breast reconstruction is of great significance to patients’ physical and mental health. However, traditional surgeries are affected by factors such as surgeons’ experience and individual differences, leading to problems like insufficient precision and uncontrollable risks. The application of AI has addressed complex challenges in breast reconstruction for breast cancer patients, including difficulties in meeting high surgical precision requirements, inadequate risk prediction and management, and deficiencies in remote rehabilitation guidance. This has thereby improved patients’ quality of life and satisfaction, reduced the burden on medical staff, and promoted the advancement of clinical medical technology. This article provided an overview of AI, summarized its current applications in breast reconstruction for breast cancer patients, and analyzed existing challenges and limitations, aiming to offer new insights and references for the future clinical application of AI in breast reconstruction.

Knee Osteoarthritis (KOA) is an irreversible degenerative disease. In the early stage, it mainly involves the cartilage, and the pathological changes of cartilage occur much earlier than its morphological changes, making conventional magnetic resonance imaging of limited value. Magnetic resonance (MR) quantitative analysis enables quantitative assessment of early articular cartilage damage based on changes in different components of the cartilage. This paper systematically reviewed the current application status of various MR quantitative imaging methods in the early diagnosis of KOA, and analyzed the advantages and limitations of different MR quantitative methods in terms of sensitivity, specificity, imaging time, and hardware requirements. It is pointed out that improvements should be made in shortening data acquisition time, improving image quality, and reducing radiofrequency energy accumulation. Additionally, the development direction of integrating this field with artificial intelligence was prospected, aiming to provide references for early intervention and efficacy monitoring of KOA.

The transcranial magnetic stimulator is a noninvasive neuromodulation device extensively utilized in fields such as psychiatry and neurology. However, its prolonged operation may lead to potential failures. This paper presents three cases of malfunctions in the Magnuro 100 transcranial magnetic stimulator. The first case involved an overheating alarm. The heat dissipation mechanism was initially analyzed to ascertain if the cooling fan was damaged. After replacement, the problem was resolved. The second case demonstrated capacitor voltage abnormality. Through an analysis of the charging principle of high-voltage energy storage capacitors, key electronic components were tested post-disassembly. It was discovered that the insulation gate bipolar transistor was damaged, and the issue was rectified upon replacement. The third case reported a failure in the motor evoked potential monitoring module, where electromyography was unable to be displayed. Upon examining the signal acquisition and transmission path, it was found that the soldering point between the synchronization interface and the circuit board was loose. The issue was fixed after resoldering. This article aims to assist equipment engineers in understanding the structural principles and troubleshooting methods of similar equipment, thereby ensuring the continuity and safety of medical diagnosis and treatment works.